@inproceedings{e775fe8d420542e8959aafade9be013c,
title = "Game-theoretic learning in a distributed-information setting: Distributed convergence to mean-centric equilibria",
abstract = "The paper considers distributed learning in large-scale games via fictitious-play type algorithms. Given a preassigned communication graph structure for information exchange among the players, this paper studies a distributed implementation of the Empirical Centroid Fictitious Play (ECFP) algorithm that is well-suited to large-scale games in terms of complexity and memory requirements. It is shown that the distributed algorithm converges to an equilibrium set denoted as the mean-centric equilibria (MCE) for a reasonably large class of games.",
author = "Brian Swenson and Soummya Kar and Jo{\~a}o Xavier",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 ; Conference date: 02-11-2014 Through 05-11-2014",
year = "2015",
month = apr,
day = "24",
doi = "10.1109/ACSSC.2014.7094739",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "1616--1620",
editor = "Matthews, \{Michael B.\}",
booktitle = "Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers",
address = "United States",
}